Reference no: EM132772222

Chapter 5:

Discussion 1: What is an artificial neural network and for what types of problems can it be used?

Discussion 2: Compare artificial and biological neural networks. What aspects of biological networks are not mimicked by artificial ones? What aspects are similar?

Discussion 3: What are the most common ANN architectures? For what types of problems can they be used?

Discussion 4: ANN can be used for both supervised and unsupervised learning. Explain how they learn in a supervised mode and in an unsupervised mode.

Exercise 6:Go to Google Scholar (scholar.google.com). Conduct a search to find two papers written in the last five years that compare and contrast multiple machine-learning methods for a given problem domain. Observe commonalities and differences among their findings and prepare a report to summarize your understanding.

Chapter 6

Discussion 1: What is deep learning? What can deep learning do that traditional machine-learning methods cannot?

Discussion 2: List and briefly explain different learning paradigms/ methods in AI.

Discussion 3: What is representation learning, and how does it relate to machine learning and deep learning?

Discussion 4: List and briefly describe the most commonly used ANN activation functions.

Discussion 5: What is MLP, and how does it work? Explain the function of summation and activation weights in MLP-type ANN.

Exercise 4: Cognitive computing has become a popular term to define and characterize the extent of the ability of machines/computers to show "intelligent" behavior. Thanks to IBM Watson and its success on Jeopardy!, cognitive computing and cognitive analytics are now part of many real- world intelligent systems. In this exercise, identify at least three application cases where cognitive computing was used to solve complex real-world problems. Summarize your findings in a professionally organized report.